國立虎尾科技大學 |

Learning Grammar Distributions With Limited Feedback.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Learning Grammar Distributions With Limited Feedback./
作者:	Budnick, Ryan Daniel.
面頁冊數:	1 online resource (189 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Linguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798380384650

Learning Grammar Distributions With Limited Feedback.
Budnick, Ryan Daniel.

Learning Grammar Distributions With Limited Feedback. - 1 online resource (189 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--University of Pennsylvania, 2023.

Includes bibliographical references

The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic learning domains. I particularly note that few recent models learn with limited feedback, which measures the amount of information brought to and taken from each learning instance.In this dissertation, I adopt a geometric lens for viewing this class of learning models. Viewing previous algorithms geometrically, I diagnose their flaws and motivate a novel, natural algorithm which can overcome those flaws while operating under limited feedback, which I call the barycentric learning model. Viewing representational theories geometrically, I apply the same learning algorithm successfully to learning problems across domains in parametric, ranked-constraint, and weighted-constraint theoretical frameworks. I apply novel formal tools to analyze the algorithm's behavior, which help us understand where the algorithm demonstrates convergence and non-convergence, as well as the dynamics of learning paths within individuals, and of language change paths across generations. The success of this model demonstrates that limited feedback suffices for a larger class of learning problems than previously known, while pointing a way forward for the formal and abstract understanding of language acquisition.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380384650Subjects--Topical Terms:

557829
Linguistics.
Subjects--Index Terms:

Language acquisitionIndex Terms--Genre/Form:

554714
Electronic books.

Learning Grammar Distributions With Limited Feedback.
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500 $a Advisor: Yang, Charles.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2023.
504 $a Includes bibliographical references
520 $a The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic learning domains. I particularly note that few recent models learn with limited feedback, which measures the amount of information brought to and taken from each learning instance.In this dissertation, I adopt a geometric lens for viewing this class of learning models. Viewing previous algorithms geometrically, I diagnose their flaws and motivate a novel, natural algorithm which can overcome those flaws while operating under limited feedback, which I call the barycentric learning model. Viewing representational theories geometrically, I apply the same learning algorithm successfully to learning problems across domains in parametric, ranked-constraint, and weighted-constraint theoretical frameworks. I apply novel formal tools to analyze the algorithm's behavior, which help us understand where the algorithm demonstrates convergence and non-convergence, as well as the dynamics of learning paths within individuals, and of language change paths across generations. The success of this model demonstrates that limited feedback suffices for a larger class of learning problems than previously known, while pointing a way forward for the formal and abstract understanding of language acquisition.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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653 $a Language acquisition
653 $a Barycentric learning model
653 $a Parameters
653 $a Grammar
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